Rationale
to create collective analysis of all experiments conducted using the L. sigmodontis infection model during my post doc in the Judi Allen lab @ University of Manchester.
This data and analysis will appear in a future planned publication which is currently under consideration. The longer term objective is for the dataset to be managed by James Parkinson and appended with data from other members of the Allen laboratory.
This Rmarkdown doc is not intended to be an exhaustive list of all figures that appear in publications, but rather a tutorial which shows how plots can be generated. For R novices, I have written a basic R Data visulaisation tutorial I created for Msc Immunology Studnets at Trinity College Dublin based upon this data is availible at https://conorisco.github.io/dataviz_immunology_r/
Description of analysis
L. sigmodontis infection
Tropical rat mites, Ornithonyssus bacoti, were feed on microfilaria positive Mongolian Jirds (Meriones unguiculatus) overnight. Fully engorged mites were then collected and incubated at 27 °C, 75% relative humidity for at least 12 days to allow development to the infective third stage larvae (L3). Mites were then crushed in media (RPMI supplemented with 5% horse serum). 25 L3s were then injected subcutaneously in the scruff of the mouse using a 23G needle in 200 μl of media. Detailed instructions on the maintenance of the life cycle (Fulton et al., 2018) and the immune responses (Finlay and Allen, 2020) L. sigmodontis have been published elsewhere.
Pleural cell, fluid, and worm isolation
Pleural exudate cells (PLEC) were obtained via washing of the pleural cavity with 2 ml ice-cold PBS followed by 6-8 ml of PBS supplemented with 2 % FCS and 2 mM EDTA using a sterile transfer pipette. Cells were filtered through a sterile pluriStrainer Mini 40 μm cells strainers (pluriSelect) into a 15 ml tube. Worms retained on the filter were removed by back washing with RPMI into a 6 well plate for counting using a dissecting microscope. PBS-only washes were centrifuged, and the supernatant stored in a biobank for future analysis by ELISA or by competitive ELISA for retinoic acid detection using Mouse Retinoic Acid ELISA kit (Cusabio) using manufactures instructions. The cell pellet of the PBS-wash was combined with the cell pellet of the PBS-FCS-EDTA washes and cells were counted using a Nexcelom automated cell counter. For a minority of samples, erythrocytes were lysed prior to cell counting.
Flow cytometry
Cells were stained in V-bottom 96 well plates in 50 µl reaction volumes kept at 4 °C. Cells were washed by addition of 200 μl of relevant buffer and centrifugated at 300-400g for 5 mins prior to fixation and 700 g for 5 mins after fixation, cells were first washed twice with PBS, and then incubated with anti-CD16/CD32 5 μg/ml (Mouse Fc Block, BD) and LIVE/DEAD Blue or LIVE/DEAD Aqua (ThermoFisher) in PBS for 30 min. Next cells were washed with PBS with 2% FCS and 2 mM EDTA (hereafter FACS buffer) and incubated with antibodies to surface antigens (0.5 µg/ml) with mouse serum (1 in 50). We used a lineage cocktail on the same fluorophore which contained TCR-, CD20, CD19, Ly6G, Nk1.1, CD90.2, CD5, IgM, CD3 and Siglec-F, with some experiments using Ly6G on another channel, and some experiments forgoing CD5 and Nk1.1. Optionally,for Intracellular antigen staining, cells were fixed and permeabilized using the Foxp3/Transcription Factor Staining Buffer Set overnight and stained with antibodies against intracellular antigens (1 µg/ml) in Permeabilization buffer (ThermoFisher). For biotinylated antibodies, fluorophore-conjugated streptavidin (Biolegend) was added in the same reaction (1 in 400 from stock). Optionally, for assessment of edU incorporation EdU was next detected with Alexa Fluor 647-azide using Click-iT Plus EdU Flow Cytometry Assay Kit (ThermoFisher) according to the manufacturer’s instructions. Samples were acquired on LSRFotessa X-20 or LSRFortessa (BD). Exclusion criteria: Samples removed from analysis if they contained staining or acquisition artefacts in key parameters if this made comparison with other samples difficult or if the number of events in the ‘live cell’ gate was less than 1000. Analysis was performed using FlowJo and gating strategies are detailed in Figure S1. Gating strategies and optimal fluorophore and antibody choices were refined through the project and alternative gating strategies exist (Figure S1). To remove staining/acquisition artefacts we used FlowAI Flowjo Plugin (Monaco et al., 2016). Anti-CD115 was not used to identify monocytes/MΦs because in our hands we observed a down regulation of CD115 by MΦs after isolation that was inconsistent between experiments.
Gating Strategy
Collective analysis of L. sigmodontis data
Measured variables from 40 independent L. sigmodontis infection experiments containing 666 (535 female and 113 male animals with a median age 124 days at end of experiment) mice were compiled into a single dataset containing mice of various backgrounds that were infected or uninfected (Naïve). Genetically modified mice which were homozygous negative (‘wild type’) for the respective mutation were also included for in the analysis. Analysis includes mice from reductionist experiments if these mice were in the control groups, that is they received i.p. injections of PBS, distilled H2O added to drinking water or isotype control antibody. Mice were then filtered to include only those on a BALB/c or C57BL/6 background. An additional 24 mice were removed from analysis for either failing to meet quality control standard of flow cytometric data (i.e. low event count, staining artefacts) or they were deemed to be not infected. We defined a failed infection as those mice which were injected with L3 larva but upon analysis had no live worms, no gramulomas, no evidence of moulted worm exoskeleton no increase in PLEC count over baseline and PLEC eosinophils <2% of PLEC. No mice were excluded after day 50 p.i. as parasite clarence rather than failed infection could explain these observations. After data filtering, 360 mice remained (311 female and 49 male, median age 103 days at end of experiment). Metadata included the following categorical data: experiment number, sex, infection status, strain, genetic background, genotype and supplier. Numerical data included: age, blood score, worm counts, granuloma counts, cell counts, live cell %, L3 infection dose, and numerical outputs from flow cytometric analysis. To ensure consistent inter-experiment flow cytometry data, all experiments retained after filtering were reanalyzed in Flowjo using the gating strategy displayed in Figure S2. Analysis was performed in R using the Tidyverse package libraries. Groups were collated by genetic background irrespective of sex, supplier, and sub-strain. Data visualization was created using the ggplot2 R package. For bar chart summary graphs infected groups included data from day 23 and day 60 p.i. Statistics were performed using the stat_compare_means() function from the ggpubr R package. For time course data, naïve samples were given the time of 0. Lines in time course graphs model a polynomial regression with locally estimated scatterplot smoothing (loess), generated using the geom_smooth() function from ggplot2.
Note mouse numbers may increase as dataset grows.
Setup
Packages
library(tidyverse)
library(readxl)
library(cowplot)
library(ggbeeswarm)
library(gtable)
library(grid)
library(gridExtra)
library(ggpubr)Load data
#Load data
library(readxl)
raw <- read_excel("../GLOBAL_2020.xlsx") Data cleanup and tidying
# Remove FACS sample name
colnames(raw) [1] "FACS Sample ID" "Global ID"
[3] "Experiment" "ICS data"
[5] "Macrophage data" "Mouse"
[7] "Experimental Group" "Infection"
[9] "Sex" "Strain"
[11] "Background" "Genotype"
[13] "Supplier" "Treatment"
[15] "OK for strain comparison" "Timepoint"
[17] "Remove from Analysis (justified)" "Day 0 Age"
[19] "Age" "L3 Larva"
[21] "Blood Score" "Granulomas"
[23] "Live worms" "% Live"
[25] "Cell Count" "B cells"
[27] "Eosinophils" "Neutrophils"
[29] "CD4+ T cells" "CD4- T cells"
[31] "T cells" "DC"
[33] "Myeloid cells" "Monocytes"
[35] "SCM" "Converting SCM"
[37] "LCM" "Monocyte of Myeloid"
[39] "SCM of Myeloid" "Converting SCM of Myeloid"
[41] "LCM of Myeloid" "B1 of B cells"
[43] "Ym1 of Macrophages" "RELMA of Macrophages"
[45] "Arg1 of Macrophages" "PDL2+ of Macrophages"
[47] "Lyve1+ of Macrophages" "Ki67 of Macrophages"
[49] "Ym1 gMFI of Macrophages" "RELMA gMFI of Macrophages"
[51] "Arg1 gMFI of Macrophages" "Arg1+ of LCM"
[53] "Ki67+ of LCM" "RELMA+ of LCM"
[55] "Ym1+ of LCM" "Arg1 gMFI of LCM"
[57] "RELMA gMFI of LCM" "Ym1 gMFI of LCM"
[59] "Tim4+ of LCM" "Ki67 of T cells"
[61] "Ki67 of B cells" "CD40L"
[63] "IFN-g" "IL-4"
[65] "IL-5" "IL-10"
[67] "IL-13" "IL-17A"
[69] "PD-1" "ST2"
[71] "CD69" "FOXP3"
[73] "Mertk+" "MertK+ LCM"
[75] "FRBeta+" raw <- raw[-1]Filtering, factoring and colour
# create an unmaniplated df called raw and factorise key parameters so they can be ordered when graphing
raw$Infection <- factor(raw$Infection,levels = c("Naive", "Infected"))
raw$Background <- factor(raw$Background,levels = c("C57BL/6", "B10.D2", "BALB/c", "F1"))
# create a dataset called data which removes poor quality readouts and only those which include lito and filters to include mid stage of infection as infected
data <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% filter(`Timepoint` < 64) %>% filter(`Timepoint` != 11) %>% filter(`Timepoint` != 23) %>% filter(`Timepoint` != 18)
# Colour palettes
cols <- c("black","firebrick3", "purple", "green")
pal <- scale_color_manual(values = cols)Graphs
Flaoting chunks to look at whatever you want
Timecourse
p1 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=`B1 of B cells` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
#ggtitle("Cell Count") +
#scale_y_log10(limits=c(1e3,2.5e7)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p1
#ggsave(filename = "PDl2_kinetics.pdf", path = "./ggsave", device = "pdf", height = 5, width =6)barchart
data %>% ggplot(aes(`B1 of B cells`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.x = element_blank(),
plot.margin = unit(c(0.5, 0, 0, 0), "cm")) + pal
publication graphs
Total cell kinetics
p1 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>%
filter(`OK for strain comparison` == "T") %>%
ggplot(aes(x=Timepoint,
y=`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("Cell Count") +
#scale_y_log10(limits=c(1e3,2.5e7)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p1
ggsave(filename = "Cell_kinetics.pdf", path = "../ggsave", device = "pdf", height = 5, width =6)Base results
p1 <- data %>% ggplot(aes(y=`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
#geom_quasirandom() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitter(width = 0.2)) +
theme_cowplot(font_size = 30) +
xlab("") +
theme(legend.position = "none") +
#scale_color_brewer(palette = "Dark2")
pal +
stat_compare_means(method = "wilcox.test", label = "p.signif", label.x.npc = "middle",size=8, vjust = 1)
p2 <- data %>% ggplot(aes(y=(`Live worms`/`L3 Larva`)*100, x=`Infection`, col=Background)) +
geom_boxplot() +
#geom_quasirandom() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitter(width = 0.2)) +
theme_cowplot(font_size = 30) +
xlab("") +
ylab("% Worm Recovery") +
theme(legend.position = "none") +
pal +
stat_compare_means(method = "t.test", label = "p.signif", label.x.npc = "middle",size=8, vjust = 1)
p3 <- data %>% ggplot(aes(y=`Blood Score`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_point(position = position_jitterdodge(jitter.height = 0.15)) +
#geom_quasirandom(method = "tukeyDense",dodge.width = 0.9) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
theme_cowplot(font_size = 30) +
xlab("") +
theme(legend.position = "none") +
pal +
stat_compare_means(method = "wilcox.test", label = "p.signif", label.x.npc = "middle",size=8, vjust = 1)
plot_grid(p2,p1,p3,align = 'h', ncol =3)
ggsave(filename = "1B.pdf", path = "../ggsave", device = "pdf", height = 5, width =15)cells
Note that repeated ggplot layers are passed as variables
t <-stat_compare_means(method = "t.test", label = "p.signif", label.x.npc = "middle",size=8, vjust = 0.25)
tight <- theme(legend.title = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",
plot.margin = unit(c(0, 0, 0, 0), "cm"),
axis.text.x=element_blank()
)
tightn <- theme(legend.title = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
legend.position = "none",
plot.margin = unit(c(0.25, 0, 0, 0), "cm"),
#axis.text.x=element_blank()
)
cow <- theme_cowplot(font_size = 30)
cols <- c("black","firebrick3", "purple", "green")
pal <- scale_color_manual(values = cols)
# Make blank plot for arrangement
blankP <- ggplot()+geom_blank(aes(1,1)) +
cowplot::theme_nothing()
p1 <- data %>% ggplot(aes(y=`B cells`, x=`Infection`, col=Background)) +
geom_boxplot() +
#geom_quasirandom() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitter(width = 0.2)) +
ggtitle("B cells") +
#ylab("% of total cells") +
cow + theme(legend.title = element_blank(),
axis.title.y = element_blank(),
axis.title.x = element_blank(),
legend.position = "top",
plot.margin = unit(c(0, 0, 0, 0), "cm"),
axis.text.x=element_blank()
) + pal + t
# Get and save legend
legend <- get_legend(p1)
ggsave(plot = legend,filename = "legend.pdf", path = "../ggsave", device = "pdf", height =1, width =5)
p1 <- p1 + theme(legend.position="none")
# rest of plots
p2 <- data %>% ggplot(aes(y=Eosinophils, x=`Infection`, col=Background)) +
geom_boxplot() +
#geom_quasirandom() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitter(width = 0.2)) +
ggtitle("Eosinophils") +
cow + tight + pal + t
p3 <- data %>% ggplot(aes(y=Neutrophils, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
ggtitle("Neutrophils") +
cow + tight + pal + t
p4 <- data %>% ggplot(aes(y=`CD4+ T cells`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
ggtitle("CD4+ T cells") +
cow + tight + pal + t
p5 <- data %>% ggplot(aes(y=DC, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
ggtitle("cDC") +
cow + tight + pal + t
p6 <- data %>% ggplot(aes(y=`Myeloid cells`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
ggtitle("Myeloid Cells") +
#ylab("") +
cow + tight + pal + t
# row2
n1 <- data %>% ggplot(aes(y=(`B cells`/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
#geom_quasirandom() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitter(width = 0.2)) +
cow + tightn + pal + t
n2 <- data %>% ggplot(aes(y=(Eosinophils/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
#geom_quasirandom() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitter(width = 0.2)) +
cow + tightn + pal + t
n3 <- data %>% ggplot(aes(y=(Neutrophils/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
cow + tightn + pal + t
n4 <- data %>% ggplot(aes(y=(`CD4+ T cells`/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
cow + tightn + pal + t
n5 <- data %>% ggplot(aes(y=(DC/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
cow + tightn + pal + t
n6 <- data %>% ggplot(aes(y=(`Myeloid cells`/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
cow + tightn + pal + t
plot_grid(
p1,p2,p3,p4,p5,p6,
n1,n2,n3,n4,n5,n6,
align = 'v', ncol =6, nrow=2)
ggsave(filename = "1E_F_wide.pdf", path = "../ggsave", device = "pdf", height = 10, width =28)Lin+ cells kinetics
p1 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=(`B cells`/100)*`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("B Cells") +
ylim(NA,25000000) +
#scale_y_log10() +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p2 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=(Eosinophils/100)*`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("Eosinophils") +
#scale_y_log10() +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p3 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=(Neutrophils/100)*`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("Neutrophils") +
#scale_y_log10() +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p4 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=(`Myeloid cells`/100)*`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("Myeloid Cells") +
#scale_y_log10() +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p5 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=(`CD4+ T cells`/100)*`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("CD4+ T cells") +
#scale_y_log10() +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
plot_grid(p1,p2,p3,p5,p4,align = 'h', ncol =5)
ggsave(filename = "Lineage_pos_kinetics.pdf", path = "../ggsave", device = "pdf", height = 6, width =30)Myeloid cell kinetics
p1 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=`Monocyte of Myeloid` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("Monocytes") +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.x=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p2 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=`SCM of Myeloid` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("SCM") +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.x=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p3 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=`Converting SCM of Myeloid` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("Converting SCM") +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.x=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p4 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=`LCM of Myeloid` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
ggtitle("LCM") +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.x=element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p5 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=(Monocytes/100)*`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
scale_y_log10(limits=c(1e3,2.5e7)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p6 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=(SCM/100)*`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
scale_y_log10(limits=c(1e3,2.5e7)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p7 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=(`Converting SCM`/100)*`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
scale_y_log10(limits=c(1e3,2.5e7)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
p8 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(x=Timepoint,
y=(LCM/100)*`Cell Count` ,col=`Background`)) + geom_point(position = position_jitterdodge(jitter.width = 10), aes()) + geom_smooth() +
scale_y_log10(limits=c(1e3,2.5e7)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.y = element_blank(),
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal
plot_grid(p2,p1,p3,p4,
p6,p5,p7,p8,
align = 'v', nrow=2, ncol =4)
ggsave(filename = "Myeloid_kinetics_percent_numbers.pdf", path = "../ggsave", device = "pdf", height = 10, width =30)Myeloid cell bargraphs
#kwargs
yl <- xlab("")
cow <- theme_cowplot(font_size = 30)
pal <- pal
tight <-theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
axis.text.x=element_blank(),
plot.margin = unit(c(0.5, 0, 0, 0), "cm"))
tightn <- theme(legend.position = "none",
axis.title.y = element_blank(),
axis.title.x = element_blank(),
plot.margin = unit(c(0.5, 0, 0, 0), "cm"))
t <-stat_compare_means(method = "t.test", label = "p.signif", label.x.npc = "middle",size=8, vjust = 0.25)
p1 <- data %>% ggplot(aes(y=`Monocyte of Myeloid`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
ggtitle("Monocytes") +
cow +yl + pal + tight + t
pal<ggproto object: Class ScaleDiscrete, Scale, gg>
aesthetics: colour
axis_order: function
break_info: function
break_positions: function
breaks: waiver
call: call
clone: function
dimension: function
drop: TRUE
expand: waiver
get_breaks: function
get_breaks_minor: function
get_labels: function
get_limits: function
guide: legend
is_discrete: function
is_empty: function
labels: waiver
limits: NULL
make_sec_title: function
make_title: function
map: function
map_df: function
n.breaks.cache: NULL
na.translate: TRUE
na.value: NA
name: waiver
palette: function
palette.cache: NULL
position: left
range: <ggproto object: Class RangeDiscrete, Range, gg>
range: NULL
reset: function
train: function
super: <ggproto object: Class RangeDiscrete, Range, gg>
rescale: function
reset: function
scale_name: manual
train: function
train_df: function
transform: function
transform_df: function
super: <ggproto object: Class ScaleDiscrete, Scale, gg>p2 <- data %>% ggplot(aes(y=`SCM of Myeloid`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
ggtitle("SCM") +
cow +yl + pal + tight + t
p3 <- data %>% ggplot(aes(y=`Converting SCM of Myeloid`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
ggtitle("Converting SCM") +
cow +yl + pal + tight + t
p4 <- data %>% ggplot(aes(y=`LCM of Myeloid`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
ggtitle("LCM") +
cow +yl + pal + tight + t
#numbers
library(scales)
n1 <- data %>% ggplot(aes(y=(`Monocyte of Myeloid`/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
ylab("Monocytes") +
scale_y_continuous(labels = scientific) +
cow +yl + pal + tightn + t
n2 <- data %>% ggplot(aes(y=(`SCM of Myeloid`/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
ylab("DC-like SCM") +
cow +yl + pal + tightn + t
n3 <- data %>% ggplot(aes(y=(`Converting SCM of Myeloid`/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
ylab("Converting SCM") +
cow +yl + pal + tightn + t
n4 <- data %>% ggplot(aes(y=(`LCM of Myeloid`/100)*`Cell Count`, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
ylab("LCM") +
cow +yl + pal + tightn + t
plot_grid(
p2,p1,p3,p4,
n2,n1,n3,n4,
align = 'v', ncol =4, nrow=2)
ggsave(filename = "2B.pdf", path = "../ggsave", device = "pdf", height = 10, width =20)LCM to recruited Ratio
n1 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(
x= Timepoint,
y=(`LCM of Myeloid`/(`Monocyte of Myeloid` + `SCM of Myeloid` + `Converting SCM of Myeloid`)),
col=`Background`)) +
geom_point(position = position_jitterdodge(jitter.width = 10),aes()) + geom_smooth() +
scale_y_log10() +
xlab("Timepoint") +
ylab("LCM:Recruited Ratio") +
#geom_text(label=raw$Experiment) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
plot.margin = unit(c(1, 0, 1, 0), "cm")) +
pal
n3 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% filter(`Infection` == "Infected") %>% filter(`Timepoint` > 30 & `Timepoint` < 65) %>% ggplot(aes(
x= (`LCM of Myeloid`/(`Monocyte of Myeloid` + `SCM of Myeloid` + `Converting SCM of Myeloid`)),
y=(`Live worms`/`L3 Larva`)*100
)) +
geom_point(aes(col= Background)) + geom_smooth(method = "lm") +
scale_x_log10() +
xlab("LCM:Recruited Ratio") +
ylab("% Worm Recovery") +
#geom_text(label=raw$Experiment) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
plot.margin = unit(c(1, 0, 1, 0), "cm")) +
pal
plot_grid(
n1,n3,
align = 'v', ncol =1, nrow=2, rel_widths = c(1,1))
ggsave(filename = "Macrophage_ratio_kinetics_worm_summary.pdf", path = "../ggsave", device = "pdf", height = 10, width =7.5)model_data <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% filter(`Infection` == "Infected") %>% filter(`Timepoint` > 30 & `Timepoint` < 65)
lmmodel = lm((`Live worms`/`L3 Larva`)~ log10((`Monocyte of Myeloid` + `SCM of Myeloid` + `Converting SCM of Myeloid`)/`LCM of Myeloid`), data = model_data, ) #Create the linear regression
summary(lmmodel) #Review the results
Call:
lm(formula = (`Live worms`/`L3 Larva`) ~ log10((`Monocyte of Myeloid` +
`SCM of Myeloid` + `Converting SCM of Myeloid`)/`LCM of Myeloid`),
data = model_data)
Residuals:
Min 1Q Median 3Q Max
-0.21670 -0.07264 -0.02162 0.05897 0.36380
Coefficients:
Estimate
(Intercept) 0.202196
log10((`Monocyte of Myeloid` + `SCM of Myeloid` + `Converting SCM of Myeloid`)/`LCM of Myeloid`) 0.126330
Std. Error
(Intercept) 0.008417
log10((`Monocyte of Myeloid` + `SCM of Myeloid` + `Converting SCM of Myeloid`)/`LCM of Myeloid`) 0.009428
t value
(Intercept) 24.02
log10((`Monocyte of Myeloid` + `SCM of Myeloid` + `Converting SCM of Myeloid`)/`LCM of Myeloid`) 13.40
Pr(>|t|)
(Intercept) <2e-16
log10((`Monocyte of Myeloid` + `SCM of Myeloid` + `Converting SCM of Myeloid`)/`LCM of Myeloid`) <2e-16
(Intercept) ***
log10((`Monocyte of Myeloid` + `SCM of Myeloid` + `Converting SCM of Myeloid`)/`LCM of Myeloid`) ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.1114 on 175 degrees of freedom
(27 observations deleted due to missingness)
Multiple R-squared: 0.5064, Adjusted R-squared: 0.5036
F-statistic: 179.5 on 1 and 175 DF, p-value: < 2.2e-16n2 <- data %>% ggplot(aes(
y=(`LCM of Myeloid`/(`Monocyte of Myeloid` + `SCM of Myeloid` + `Converting SCM of Myeloid`)),
x=`Infection`, col=Background)) +
geom_boxplot() +
scale_y_log10() +
ylab("LCM:Recruited Ratio") +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.x = element_blank(),
plot.margin = unit(c(1, 0.5, 1, 0), "cm")) +
pal +
stat_compare_means(method = "t.test", label = "p.signif", label.x.npc = "middle",size=8, vjust = 1)
n2
ggsave(filename = "Macrophage_ratio_bar_summary.pdf", path = "../ggsave", device = "pdf", height = 5, width =7.5)Tim4
n3 <- data %>% ggplot(aes(y=
`Tim4+ of LCM`
, x=`Infection`, col=Background)) +
geom_boxplot() +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
theme_cowplot(font_size = 30) +
xlab("") +
ylab("% Tim4+") +
theme(legend.position = "none",
plot.margin = unit(c(0.5, 0, 0, 0), "cm")) +
pal + t
p2 <- raw %>% filter(`Remove from Analysis (justified)` != "T") %>% filter(`OK for strain comparison` == "T") %>% ggplot(aes(
x= Timepoint,
y=`Tim4+ of LCM` ,
col=`Background`)) +
geom_point(position = position_jitterdodge(jitter.width = 10),aes()) + geom_smooth() +
xlab("Timepoint") +
ylab("% Tim4+") +
#geom_text(label=raw$Experiment) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
plot.margin = unit(c(0, 0, 0, 0), "cm")) +
pal +t
plot_grid(n3,p2, align = 'v', nrow =2, ncol = 1, rel_widths=c(0.75,1), label_y = "% Tim4+")
plot_grid(n3,p2, nrow =2, ncol = 1)
ggsave(filename = "Tim4.pdf", path = "../ggsave", device = "pdf", height = 10, width =7.5)Naive cells only
p3<-raw %>%
filter(`Remove from Analysis (justified)` != "T") %>%
filter(`OK for strain comparison` == "T") %>%
filter(Infection == "Naive") %>%
ggplot(aes(
y=(`LCM of Myeloid`/`SCM of Myeloid` ),
x=`Background`, col=Background)) +
geom_boxplot() +
scale_y_log10() +
ylab("LCM:SCM ratio") +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
coord_fixed(ratio = 1.3) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.x = element_blank(),
plot.margin = unit(c(1, 0.5, 1, 0), "cm")) +
pal +
stat_compare_means(method = "t.test", label = "p.signif", label.x.npc = "middle",size=8, vjust = 1)
p1<-raw %>%
filter(`Remove from Analysis (justified)` != "T") %>%
filter(`OK for strain comparison` == "T") %>%
filter(Infection == "Naive") %>%
ggplot(aes(
y=((`LCM of Myeloid`/100)*`Cell Count` ),
x=`Background`, col=Background)) +
geom_boxplot() +
scale_y_log10() +
ylab("LCM") +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
coord_fixed(ratio = 1.3) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.x = element_blank(),
plot.margin = unit(c(1, 0.5, 1, 0), "cm")) +
pal +
stat_compare_means(method = "t.test", label = "p.signif", label.x.npc = "middle",size=8, vjust = 1)
p2 <-raw %>%
filter(`Remove from Analysis (justified)` != "T") %>%
filter(`OK for strain comparison` == "T") %>%
filter(Infection == "Naive") %>%
ggplot(aes(
y=((`SCM of Myeloid`/100)*`Cell Count` ),
x=`Background`, col=Background)) +
geom_boxplot() +
scale_y_log10() +
ylab("SCM") +
geom_quasirandom(method = "tukeyDense",dodge.width = 0.75) +
coord_fixed(ratio = 1.3) +
#geom_point(position = position_jitterdodge(dodge.width = 0.75, jitter.width = 0.2)) +
theme_cowplot(font_size = 30) +
theme(legend.position = "none",
axis.title.x = element_blank(),
plot.margin = unit(c(1, 0.5, 1, 0), "cm")) +
pal +
stat_compare_means(method = "t.test", label = "p.signif", label.x.npc = "middle",size=8, vjust = 1)
plot_grid(p1,p2,p3,align = 'hv', ncol =3)
ggsave(filename = "Naive_comparison.pdf", path = "../ggsave", device = "pdf", height = 7, width =16)